The present invention relates to detection of deformable structures in medical images, and more particularly, to detection of deformable structures in medical images using a probabilistic, hierarchical, and discriminant framework.
Rapid and accurate detection of deformable structures in medical images is a difficult task. Deformable structures in medical images are anatomic structures with non-rigid boundaries. Since deformable anatomic structures are non-rigid, it is necessary to explore a high dimensional configuration space to detect the shape of deformable structures. Furthermore, the anatomy appearance variation is large in deformable structures, such that the shape of a deformable structure from one patient cannot be rigidly transformed to determine the shape of the deformable structure in another patient. This results in a complex appearance model for deformable structures. Additionally, speed and accuracy requirements for the detection of deformable structures in medical images pose additional challenges.
The use of generative models and energy minimization methods to detect deformable structures is widely studied. Classic deformable models seek a parameterized curve that minimizes a cost function based on a gradient operator, assuming that the edge defines the curve. In P. Feizenszwalb et al., “Representation and Detection of Deformable Shapes”, IEEE Trans. PAMI, 27, 2005, a deformable shape is represented using triangulated polygons, which are fitted to the shape using energy minimization. In S. Sclaroff et al., “Deformable Shape Detection and Description via Model-Based Region Grouping”, IEEE Trans. PAMI, 23:475, model-based region grouping is used to find a deformable template, while in J. Coughlan et al., “Finding Deformable Shapes Using Loopy Belief Propagation”, In European Conf. Computer Vision, 2002, loopy belief propagation is used. Disadvantages of using the above generative models to detect deformable structures include their need for initialization and slow fitting speeds.